diff --git a/examples/offline_inference/vision_language_multi_image.py b/examples/offline_inference/vision_language_multi_image.py
index 98a73916..0493222d 100644
--- a/examples/offline_inference/vision_language_multi_image.py
+++ b/examples/offline_inference/vision_language_multi_image.py
@@ -229,8 +229,8 @@ def load_mllama(question: str, image_urls: list[str]) -> ModelRequestData:
         limit_mm_per_prompt={"image": len(image_urls)},
     )
 
-    placeholders = "<|image|>" * len(image_urls)
-    prompt = f"{placeholders}<|begin_of_text|>{question}"
+    img_prompt = "Given the first image <|image|> and the second image<|image|>"
+    prompt = f"<|begin_of_text|>{img_prompt}, {question}?"
     return ModelRequestData(
         engine_args=engine_args,
         prompt=prompt,
diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py
index ac4bdbc4..68d5298d 100644
--- a/vllm/model_executor/models/mllama.py
+++ b/vllm/model_executor/models/mllama.py
@@ -180,10 +180,10 @@ class MllamaMultiModalProcessor(EncDecMultiModalProcessor[MllamaProcessingInfo]
         mm_inputs = super().apply(prompt, mm_data, hf_processor_mm_kwargs,
                                   return_mm_hashes)
 
+        image_token_id = self.info.get_hf_config().image_token_index
         # Check that the number of image tokens in the decoder prompt matches
         # the number of images provided in mm_data
-        num_image_tokens = mm_inputs['prompt_token_ids'].count(
-            self.info.get_hf_config().image_token_index)
+        num_image_tokens = mm_inputs['prompt_token_ids'].count(image_token_id)
         image_data = mm_data.get("image", [])
         num_images = 1 if isinstance(image_data, Image) else len(image_data)
         if num_image_tokens != num_images:
@@ -191,8 +191,55 @@ class MllamaMultiModalProcessor(EncDecMultiModalProcessor[MllamaProcessingInfo]
                 f"The number of image tokens ({num_image_tokens}) must be"
                 f" the same as the number of images ({num_images})")
 
+        # Given prompt: <IMG0> P0 P1 <IMG1> <IMG2> P3 P4 D5 D6...., (P-prefill, D-decode)  # noqa: E501
+        # P0 & P1 do cross attention with placeholder of <IMG0>
+        # P3 P4 D5 D6 do cross attention with placeholder of <IMG1> and <IMG2>
+        # Example input to encoder and decoder:
+        # {
+        #     'encoder': {
+        #         'type': 'token',
+        #         'prompt_token_ids': [128256, 128256, ..., 128256],
+        #         'prompt': '<|image|><|image|>...<|image|>',
+        #         'multi_modal_data': {'image': <PIL.Image.Image image mode=RGB size=1770x1180 at 0x7FDE2C624880>},  # noqa: E501
+        #     },
+        #     'decoder': {
+        #         'type': 'token',
+        #         'prompt_token_ids': [128000, 128256, 128000, 3923, 374, 279, 2262, 315, 420, 2217, 30],  # noqa: E501
+        #         'prompt': '<|image|><|begin_of_text|>What is the content of this image?',  # noqa: E501
+        #         'multi_modal_data': {'image': <PIL.Image.Image image mode=RGB size=1770x1180 at 0x7FDE2C624880>},  # noqa: E501
+        #     },
+        # }
+
+        if mm_data:
+            # Since only the last group of consecutive images
+            # are attended by the decoded tokens, we only need to
+            # get the number of tokens for those images.
+            token_per_chunk = self.info.get_token_per_chunk_from_config()
+            num_decode_images = self._get_num_image_in_last_group(
+                mm_inputs["prompt_token_ids"])
+            num_encode_images = num_images - num_decode_images
+
+            # Set encoder prompt length based on the number of tiles.
+            # This tells the block manager to allocate correct number
+            # of slots for encoder tokens.
+            num_tiles = mm_inputs["mm_kwargs"]["num_tiles"]
+            decode_tiles = num_tiles[num_encode_images:num_images].sum().item()
+            num_tokens = decode_tiles * token_per_chunk
+            mm_inputs["encoder_prompt_token_ids"] = [image_token_id
+                                                     ] * num_tokens
+            mm_inputs["encoder_prompt"] = "<|image|>" * num_tokens
+
         return mm_inputs
 
+    def _get_num_image_in_last_group(self, prompt_token_ids: List[int]) -> int:
+        num_images = 0
+        for token_id in prompt_token_ids[::-1]:
+            if token_id == self.info.get_hf_config().image_token_index:
+                num_images += 1
+            elif num_images > 0:
+                break
+        return num_images
+
     def _call_hf_processor(
         self,
         prompt: str,
@@ -210,19 +257,7 @@ class MllamaMultiModalProcessor(EncDecMultiModalProcessor[MllamaProcessingInfo]
             processed_outputs["num_tiles"] = torch.tensor(num_tiles)
             for k in ('pixel_values', 'aspect_ratio_ids', "aspect_ratio_mask"):
                 processed_outputs[k] = processed_outputs[k].squeeze(0)
-            # Example input to encoder and decoder:
-            # {
-            #     'encoder': {
-            #         'type': 'token',
-            #         'prompt_token_ids': [128256, 128000, 3923, 374, 279, 2262, 315, 420, 2217, 30],  # noqa: E501
-            #         'prompt': '<|image|><|begin_of_text|>What is the content of this image?',  # noqa: E501
-            #         'multi_modal_data': {'image': <PIL.Image.Image image mode=RGB size=1770x1180 at 0x7FDE2C624880>},  # noqa: E501
-            #     },
-            #     'decoder': {
-            #         'type': 'token',
-            #         'prompt_token_ids': [128000],
-            #     },
-            # }
+
             processed_token_ids = processed_outputs.pop("input_ids")
             start_idx, end_idx = 0, processed_token_ids.size(1)
             processed_prompt_text = tokenizer.decode(processed_token_ids[0])